supermarket
Gasha Sarwono
4/4/2021
1. Background
The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data.
With this data I will analyze and show visualization in intereactive graph/plot about:
Analyze 1 : Frequency sales quantity in each city (Frekuensi kuantitas penjualan di masing-masing kota)
Analyze 2 : Frequency type payment purchasing on each month (Frekuensi tipe pembayaran pembelian disetiap bulannya)
Analyze 3 : Products with the most and least sales of each gender (Produk dengan penjualan paling banyak dan paling sedikit pada setiap gender)
My source raw data its from Kaggle:
2. Set Up
Import Data
raw_data_supermarket <- read.csv("supermarket_sales - Sheet1.csv", sep=",")
head(raw_data_supermarket)## Invoice.ID Branch City Customer.type Gender Product.line
## 1 750-67-8428 A Yangon Member Female Health and beauty
## 2 226-31-3081 C Naypyitaw Normal Female Electronic accessories
## 3 631-41-3108 A Yangon Normal Male Home and lifestyle
## 4 123-19-1176 A Yangon Member Male Health and beauty
## 5 373-73-7910 A Yangon Normal Male Sports and travel
## 6 699-14-3026 C Naypyitaw Normal Male Electronic accessories
## Unit.price Quantity Tax.5. Total Date Time Payment cogs
## 1 74.69 7 26.1415 548.9715 1/5/2019 13:08 Ewallet 522.83
## 2 15.28 5 3.8200 80.2200 3/8/2019 10:29 Cash 76.40
## 3 46.33 7 16.2155 340.5255 3/3/2019 13:23 Credit card 324.31
## 4 58.22 8 23.2880 489.0480 1/27/2019 20:33 Ewallet 465.76
## 5 86.31 7 30.2085 634.3785 2/8/2019 10:37 Ewallet 604.17
## 6 85.39 7 29.8865 627.6165 3/25/2019 18:30 Ewallet 597.73
## gross.margin.percentage gross.income Rating
## 1 4.761905 26.1415 9.1
## 2 4.761905 3.8200 9.6
## 3 4.761905 16.2155 7.4
## 4 4.761905 23.2880 8.4
## 5 4.761905 30.2085 5.3
## 6 4.761905 29.8865 4.1Activated Library
library(flexdashboard)
library(tidyverse)
library(plotly)
library(scales)
library(lubridate)3. Subsetting Data
Choose data needed for analysis
supermarket <-
raw_data_supermarket %>%
select("City","Quantity","Gender","Product.line", "Date", "Payment")
head(supermarket)## City Quantity Gender Product.line Date Payment
## 1 Yangon 7 Female Health and beauty 1/5/2019 Ewallet
## 2 Naypyitaw 5 Female Electronic accessories 3/8/2019 Cash
## 3 Yangon 7 Male Home and lifestyle 3/3/2019 Credit card
## 4 Yangon 8 Male Health and beauty 1/27/2019 Ewallet
## 5 Yangon 7 Male Sports and travel 2/8/2019 Ewallet
## 6 Naypyitaw 7 Male Electronic accessories 3/25/2019 Ewallet4. Data InspectIon
Check data type
glimpse(supermarket)## Rows: 1,000
## Columns: 6
## $ City <chr> "Yangon", "Naypyitaw", "Yangon", "Yangon", "Yangon", "...
## $ Quantity <int> 7, 5, 7, 8, 7, 7, 6, 10, 2, 3, 4, 4, 5, 10, 10, 6, 7, ...
## $ Gender <chr> "Female", "Female", "Male", "Male", "Male", "Male", "F...
## $ Product.line <chr> "Health and beauty", "Electronic accessories", "Home a...
## $ Date <chr> "1/5/2019", "3/8/2019", "3/3/2019", "1/27/2019", "2/8/...
## $ Payment <chr> "Ewallet", "Cash", "Credit card", "Ewallet", "Ewallet"...Change data type
supermarket$City <- as.factor(supermarket$City)
supermarket$Gender <- as.factor(supermarket$Gender)
supermarket$Product.line <- as.factor(supermarket$Product.line)
supermarket$Date <- as.Date(supermarket$Date,"%m/%d/%y")
supermarket$Payment <- as.factor(supermarket$Payment)
glimpse(supermarket)## Rows: 1,000
## Columns: 6
## $ City <fct> Yangon, Naypyitaw, Yangon, Yangon, Yangon, Naypyitaw, ...
## $ Quantity <int> 7, 5, 7, 8, 7, 7, 6, 10, 2, 3, 4, 4, 5, 10, 10, 6, 7, ...
## $ Gender <fct> Female, Female, Male, Male, Male, Male, Female, Female...
## $ Product.line <fct> Health and beauty, Electronic accessories, Home and li...
## $ Date <date> 2020-01-05, 2020-03-08, 2020-03-03, 2020-01-27, 2020-...
## $ Payment <fct> Ewallet, Cash, Credit card, Ewallet, Ewallet, Ewallet,...Check Missing Value
colSums(is.na(supermarket))## City Quantity Gender Product.line Date Payment
## 0 0 0 0 0 0Data no have missing value
5. Subsetting dan Aggregation
Subsetting dan Aggregation data dilakukan untuk memilih dan mengolah data yang akan kita gunakan untuk visualisasi
5.1 Total sales quantity in each city (Jumlah kuantitas penjualan di masing-masing kota)
Make variabel “data1” to analysis visualization
data1 <- supermarket[,c("City","Quantity","Date")]
data1 <- as.data.frame(table(data1$Date,data1$City))
data1 <- setNames(data1,c("Date","City","Quantity"))
data1$Date <- ymd(data1$Date)
data1## Date City Quantity
## 1 2020-01-01 Mandalay 3
## 2 2020-01-02 Mandalay 4
## 3 2020-01-03 Mandalay 3
## 4 2020-01-04 Mandalay 3
## 5 2020-01-05 Mandalay 3
## 6 2020-01-06 Mandalay 2
## 7 2020-01-07 Mandalay 3
## 8 2020-01-08 Mandalay 6
## 9 2020-01-09 Mandalay 2
## 10 2020-01-10 Mandalay 3
## 11 2020-01-11 Mandalay 0
## 12 2020-01-12 Mandalay 6
## 13 2020-01-13 Mandalay 3
## 14 2020-01-14 Mandalay 5
## 15 2020-01-15 Mandalay 5
## 16 2020-01-16 Mandalay 3
## 17 2020-01-17 Mandalay 4
## 18 2020-01-18 Mandalay 2
## 19 2020-01-19 Mandalay 3
## 20 2020-01-20 Mandalay 2
## 21 2020-01-21 Mandalay 1
## 22 2020-01-22 Mandalay 2
## 23 2020-01-23 Mandalay 0
## 24 2020-01-24 Mandalay 6
## 25 2020-01-25 Mandalay 7
## 26 2020-01-26 Mandalay 10
## 27 2020-01-27 Mandalay 2
## 28 2020-01-28 Mandalay 6
## 29 2020-01-29 Mandalay 3
## 30 2020-01-30 Mandalay 2
## 31 2020-01-31 Mandalay 7
## 32 2020-02-01 Mandalay 0
## 33 2020-02-02 Mandalay 4
## 34 2020-02-03 Mandalay 5
## 35 2020-02-04 Mandalay 4
## 36 2020-02-05 Mandalay 3
## 37 2020-02-06 Mandalay 5
## 38 2020-02-07 Mandalay 6
## 39 2020-02-08 Mandalay 6
## 40 2020-02-09 Mandalay 4
## 41 2020-02-10 Mandalay 4
## 42 2020-02-11 Mandalay 3
## 43 2020-02-12 Mandalay 3
## 44 2020-02-13 Mandalay 3
## 45 2020-02-14 Mandalay 2
## 46 2020-02-15 Mandalay 7
## 47 2020-02-16 Mandalay 4
## 48 2020-02-17 Mandalay 2
## 49 2020-02-18 Mandalay 1
## 50 2020-02-19 Mandalay 1
## 51 2020-02-20 Mandalay 6
## 52 2020-02-21 Mandalay 2
## 53 2020-02-22 Mandalay 5
## 54 2020-02-23 Mandalay 4
## 55 2020-02-24 Mandalay 3
## 56 2020-02-25 Mandalay 8
## 57 2020-02-26 Mandalay 6
## 58 2020-02-27 Mandalay 6
## 59 2020-02-28 Mandalay 2
## 60 2020-03-01 Mandalay 5
## 61 2020-03-02 Mandalay 9
## 62 2020-03-03 Mandalay 3
## 63 2020-03-04 Mandalay 1
## 64 2020-03-05 Mandalay 10
## 65 2020-03-06 Mandalay 6
## 66 2020-03-07 Mandalay 2
## 67 2020-03-08 Mandalay 1
## 68 2020-03-09 Mandalay 6
## 69 2020-03-10 Mandalay 3
## 70 2020-03-11 Mandalay 3
## 71 2020-03-12 Mandalay 5
## 72 2020-03-13 Mandalay 3
## 73 2020-03-14 Mandalay 5
## 74 2020-03-15 Mandalay 8
## 75 2020-03-16 Mandalay 3
## 76 2020-03-17 Mandalay 2
## 77 2020-03-18 Mandalay 2
## 78 2020-03-19 Mandalay 3
## 79 2020-03-20 Mandalay 6
## 80 2020-03-21 Mandalay 1
## 81 2020-03-22 Mandalay 3
## 82 2020-03-23 Mandalay 1
## 83 2020-03-24 Mandalay 4
## 84 2020-03-25 Mandalay 2
## 85 2020-03-26 Mandalay 3
## 86 2020-03-27 Mandalay 4
## 87 2020-03-28 Mandalay 1
## 88 2020-03-29 Mandalay 4
## 89 2020-03-30 Mandalay 3
## 90 2020-01-01 Naypyitaw 4
## 91 2020-01-02 Naypyitaw 2
## 92 2020-01-03 Naypyitaw 2
## 93 2020-01-04 Naypyitaw 1
## 94 2020-01-05 Naypyitaw 4
## 95 2020-01-06 Naypyitaw 2
## 96 2020-01-07 Naypyitaw 3
## 97 2020-01-08 Naypyitaw 8
## 98 2020-01-09 Naypyitaw 5
## 99 2020-01-10 Naypyitaw 3
## 100 2020-01-11 Naypyitaw 2
## 101 2020-01-12 Naypyitaw 2
## 102 2020-01-13 Naypyitaw 3
## 103 2020-01-14 Naypyitaw 6
## 104 2020-01-15 Naypyitaw 3
## 105 2020-01-16 Naypyitaw 5
## 106 2020-01-17 Naypyitaw 3
## 107 2020-01-18 Naypyitaw 3
## 108 2020-01-19 Naypyitaw 5
## 109 2020-01-20 Naypyitaw 4
## 110 2020-01-21 Naypyitaw 2
## 111 2020-01-22 Naypyitaw 3
## 112 2020-01-23 Naypyitaw 10
## 113 2020-01-24 Naypyitaw 2
## 114 2020-01-25 Naypyitaw 5
## 115 2020-01-26 Naypyitaw 4
## 116 2020-01-27 Naypyitaw 6
## 117 2020-01-28 Naypyitaw 6
## 118 2020-01-29 Naypyitaw 4
## 119 2020-01-30 Naypyitaw 6
## 120 2020-01-31 Naypyitaw 4
## 121 2020-02-01 Naypyitaw 3
## 122 2020-02-02 Naypyitaw 6
## 123 2020-02-03 Naypyitaw 4
## 124 2020-02-04 Naypyitaw 2
## 125 2020-02-05 Naypyitaw 4
## 126 2020-02-06 Naypyitaw 6
## 127 2020-02-07 Naypyitaw 9
## 128 2020-02-08 Naypyitaw 3
## 129 2020-02-09 Naypyitaw 6
## 130 2020-02-10 Naypyitaw 3
## 131 2020-02-11 Naypyitaw 3
## 132 2020-02-12 Naypyitaw 2
## 133 2020-02-13 Naypyitaw 3
## 134 2020-02-14 Naypyitaw 3
## 135 2020-02-15 Naypyitaw 7
## 136 2020-02-16 Naypyitaw 3
## 137 2020-02-17 Naypyitaw 4
## 138 2020-02-18 Naypyitaw 3
## 139 2020-02-19 Naypyitaw 4
## 140 2020-02-20 Naypyitaw 1
## 141 2020-02-21 Naypyitaw 3
## 142 2020-02-22 Naypyitaw 3
## 143 2020-02-23 Naypyitaw 3
## 144 2020-02-24 Naypyitaw 4
## 145 2020-02-25 Naypyitaw 2
## 146 2020-02-26 Naypyitaw 1
## 147 2020-02-27 Naypyitaw 3
## 148 2020-02-28 Naypyitaw 2
## 149 2020-03-01 Naypyitaw 2
## 150 2020-03-02 Naypyitaw 6
## 151 2020-03-03 Naypyitaw 6
## 152 2020-03-04 Naypyitaw 2
## 153 2020-03-05 Naypyitaw 5
## 154 2020-03-06 Naypyitaw 1
## 155 2020-03-07 Naypyitaw 4
## 156 2020-03-08 Naypyitaw 6
## 157 2020-03-09 Naypyitaw 4
## 158 2020-03-10 Naypyitaw 3
## 159 2020-03-11 Naypyitaw 3
## 160 2020-03-12 Naypyitaw 5
## 161 2020-03-13 Naypyitaw 4
## 162 2020-03-14 Naypyitaw 9
## 163 2020-03-15 Naypyitaw 1
## 164 2020-03-16 Naypyitaw 3
## 165 2020-03-17 Naypyitaw 2
## 166 2020-03-18 Naypyitaw 3
## 167 2020-03-19 Naypyitaw 7
## 168 2020-03-20 Naypyitaw 3
## 169 2020-03-21 Naypyitaw 1
## 170 2020-03-22 Naypyitaw 0
## 171 2020-03-23 Naypyitaw 4
## 172 2020-03-24 Naypyitaw 5
## 173 2020-03-25 Naypyitaw 3
## 174 2020-03-26 Naypyitaw 4
## 175 2020-03-27 Naypyitaw 1
## 176 2020-03-28 Naypyitaw 3
## 177 2020-03-29 Naypyitaw 2
## 178 2020-03-30 Naypyitaw 4
## 179 2020-01-01 Yangon 5
## 180 2020-01-02 Yangon 2
## 181 2020-01-03 Yangon 3
## 182 2020-01-04 Yangon 2
## 183 2020-01-05 Yangon 5
## 184 2020-01-06 Yangon 5
## 185 2020-01-07 Yangon 3
## 186 2020-01-08 Yangon 4
## 187 2020-01-09 Yangon 1
## 188 2020-01-10 Yangon 3
## 189 2020-01-11 Yangon 6
## 190 2020-01-12 Yangon 3
## 191 2020-01-13 Yangon 4
## 192 2020-01-14 Yangon 2
## 193 2020-01-15 Yangon 5
## 194 2020-01-16 Yangon 2
## 195 2020-01-17 Yangon 4
## 196 2020-01-18 Yangon 4
## 197 2020-01-19 Yangon 8
## 198 2020-01-20 Yangon 4
## 199 2020-01-21 Yangon 5
## 200 2020-01-22 Yangon 2
## 201 2020-01-23 Yangon 7
## 202 2020-01-24 Yangon 5
## 203 2020-01-25 Yangon 5
## 204 2020-01-26 Yangon 3
## 205 2020-01-27 Yangon 6
## 206 2020-01-28 Yangon 2
## 207 2020-01-29 Yangon 5
## 208 2020-01-30 Yangon 1
## 209 2020-01-31 Yangon 3
## 210 2020-02-01 Yangon 3
## 211 2020-02-02 Yangon 4
## 212 2020-02-03 Yangon 5
## 213 2020-02-04 Yangon 5
## 214 2020-02-05 Yangon 5
## 215 2020-02-06 Yangon 2
## 216 2020-02-07 Yangon 5
## 217 2020-02-08 Yangon 3
## 218 2020-02-09 Yangon 3
## 219 2020-02-10 Yangon 4
## 220 2020-02-11 Yangon 2
## 221 2020-02-12 Yangon 3
## 222 2020-02-13 Yangon 2
## 223 2020-02-14 Yangon 3
## 224 2020-02-15 Yangon 5
## 225 2020-02-16 Yangon 1
## 226 2020-02-17 Yangon 7
## 227 2020-02-18 Yangon 3
## 228 2020-02-19 Yangon 4
## 229 2020-02-20 Yangon 3
## 230 2020-02-21 Yangon 1
## 231 2020-02-22 Yangon 3
## 232 2020-02-23 Yangon 1
## 233 2020-02-24 Yangon 2
## 234 2020-02-25 Yangon 6
## 235 2020-02-26 Yangon 2
## 236 2020-02-27 Yangon 5
## 237 2020-02-28 Yangon 2
## 238 2020-03-01 Yangon 3
## 239 2020-03-02 Yangon 3
## 240 2020-03-03 Yangon 5
## 241 2020-03-04 Yangon 9
## 242 2020-03-05 Yangon 2
## 243 2020-03-06 Yangon 4
## 244 2020-03-07 Yangon 3
## 245 2020-03-08 Yangon 4
## 246 2020-03-09 Yangon 6
## 247 2020-03-10 Yangon 6
## 248 2020-03-11 Yangon 5
## 249 2020-03-12 Yangon 2
## 250 2020-03-13 Yangon 3
## 251 2020-03-14 Yangon 4
## 252 2020-03-15 Yangon 3
## 253 2020-03-16 Yangon 3
## 254 2020-03-17 Yangon 2
## 255 2020-03-18 Yangon 2
## 256 2020-03-19 Yangon 6
## 257 2020-03-20 Yangon 6
## 258 2020-03-21 Yangon 4
## 259 2020-03-22 Yangon 7
## 260 2020-03-23 Yangon 6
## 261 2020-03-24 Yangon 2
## 262 2020-03-25 Yangon 4
## 263 2020-03-26 Yangon 6
## 264 2020-03-27 Yangon 5
## 265 2020-03-28 Yangon 6
## 266 2020-03-29 Yangon 2
## 267 2020-03-30 Yangon 4Make plot based on data1
plot1 <- ggplot(data1, aes(x = Date, y = Quantity, color = City)) +
geom_line()+geom_point()+
scale_x_date(date_breaks = "10 day",date_labels = "%b-%d") +
scale_y_continuous(breaks = seq(0,20,2))+
theme(legend.position = "bottom") +
theme_minimal() +
labs(title = "Frequency Sales Quantity On Each City",
x = "Date", y = "Quantity")
plot1
Make Interactive plot based on plot1
ggplotly(plot1)5.2 Frequency type payment purchasing on each month (Frekuensi tipe pembayaran pembelian disetiap bulannya)
Make variabel “data2” to analysis visualization
data2 <- supermarket[,c("Date","Payment")]
data2 <- as.data.frame(table(data2$Date,data2$Payment))
data2 <- setNames(data2,c("Date","Payment","Quantity"))
data2$Date <- ymd(data2$Date)
data2$Month <- month(data2$Date,label=T,abbr = F)
data2## Date Payment Quantity Month
## 1 2020-01-01 Cash 6 January
## 2 2020-01-02 Cash 4 January
## 3 2020-01-03 Cash 2 January
## 4 2020-01-04 Cash 3 January
## 5 2020-01-05 Cash 1 January
## 6 2020-01-06 Cash 5 January
## 7 2020-01-07 Cash 3 January
## 8 2020-01-08 Cash 6 January
## 9 2020-01-09 Cash 4 January
## 10 2020-01-10 Cash 3 January
## 11 2020-01-11 Cash 2 January
## 12 2020-01-12 Cash 1 January
## 13 2020-01-13 Cash 4 January
## 14 2020-01-14 Cash 3 January
## 15 2020-01-15 Cash 6 January
## 16 2020-01-16 Cash 5 January
## 17 2020-01-17 Cash 1 January
## 18 2020-01-18 Cash 3 January
## 19 2020-01-19 Cash 4 January
## 20 2020-01-20 Cash 2 January
## 21 2020-01-21 Cash 5 January
## 22 2020-01-22 Cash 2 January
## 23 2020-01-23 Cash 8 January
## 24 2020-01-24 Cash 5 January
## 25 2020-01-25 Cash 7 January
## 26 2020-01-26 Cash 8 January
## 27 2020-01-27 Cash 5 January
## 28 2020-01-28 Cash 4 January
## 29 2020-01-29 Cash 5 January
## 30 2020-01-30 Cash 1 January
## 31 2020-01-31 Cash 4 January
## 32 2020-02-01 Cash 2 February
## 33 2020-02-02 Cash 8 February
## 34 2020-02-03 Cash 7 February
## 35 2020-02-04 Cash 3 February
## 36 2020-02-05 Cash 4 February
## 37 2020-02-06 Cash 1 February
## 38 2020-02-07 Cash 8 February
## 39 2020-02-08 Cash 5 February
## 40 2020-02-09 Cash 7 February
## 41 2020-02-10 Cash 4 February
## 42 2020-02-11 Cash 3 February
## 43 2020-02-12 Cash 3 February
## 44 2020-02-13 Cash 2 February
## 45 2020-02-14 Cash 3 February
## 46 2020-02-15 Cash 8 February
## 47 2020-02-16 Cash 3 February
## 48 2020-02-17 Cash 4 February
## 49 2020-02-18 Cash 4 February
## 50 2020-02-19 Cash 3 February
## 51 2020-02-20 Cash 2 February
## 52 2020-02-21 Cash 5 February
## 53 2020-02-22 Cash 3 February
## 54 2020-02-23 Cash 3 February
## 55 2020-02-24 Cash 3 February
## 56 2020-02-25 Cash 5 February
## 57 2020-02-26 Cash 2 February
## 58 2020-02-27 Cash 3 February
## 59 2020-02-28 Cash 4 February
## 60 2020-03-01 Cash 1 March
## 61 2020-03-02 Cash 4 March
## 62 2020-03-03 Cash 5 March
## 63 2020-03-04 Cash 8 March
## 64 2020-03-05 Cash 2 March
## 65 2020-03-06 Cash 2 March
## 66 2020-03-07 Cash 2 March
## 67 2020-03-08 Cash 3 March
## 68 2020-03-09 Cash 8 March
## 69 2020-03-10 Cash 0 March
## 70 2020-03-11 Cash 3 March
## 71 2020-03-12 Cash 3 March
## 72 2020-03-13 Cash 3 March
## 73 2020-03-14 Cash 5 March
## 74 2020-03-15 Cash 4 March
## 75 2020-03-16 Cash 5 March
## 76 2020-03-17 Cash 2 March
## 77 2020-03-18 Cash 1 March
## 78 2020-03-19 Cash 4 March
## 79 2020-03-20 Cash 9 March
## 80 2020-03-21 Cash 0 March
## 81 2020-03-22 Cash 3 March
## 82 2020-03-23 Cash 1 March
## 83 2020-03-24 Cash 6 March
## 84 2020-03-25 Cash 5 March
## 85 2020-03-26 Cash 4 March
## 86 2020-03-27 Cash 3 March
## 87 2020-03-28 Cash 3 March
## 88 2020-03-29 Cash 4 March
## 89 2020-03-30 Cash 7 March
## 90 2020-01-01 Credit card 4 January
## 91 2020-01-02 Credit card 3 January
## 92 2020-01-03 Credit card 3 January
## 93 2020-01-04 Credit card 2 January
## 94 2020-01-05 Credit card 7 January
## 95 2020-01-06 Credit card 1 January
## 96 2020-01-07 Credit card 4 January
## 97 2020-01-08 Credit card 7 January
## 98 2020-01-09 Credit card 1 January
## 99 2020-01-10 Credit card 3 January
## 100 2020-01-11 Credit card 4 January
## 101 2020-01-12 Credit card 4 January
## 102 2020-01-13 Credit card 4 January
## 103 2020-01-14 Credit card 5 January
## 104 2020-01-15 Credit card 4 January
## 105 2020-01-16 Credit card 4 January
## 106 2020-01-17 Credit card 5 January
## 107 2020-01-18 Credit card 2 January
## 108 2020-01-19 Credit card 6 January
## 109 2020-01-20 Credit card 2 January
## 110 2020-01-21 Credit card 2 January
## 111 2020-01-22 Credit card 2 January
## 112 2020-01-23 Credit card 3 January
## 113 2020-01-24 Credit card 2 January
## 114 2020-01-25 Credit card 4 January
## 115 2020-01-26 Credit card 4 January
## 116 2020-01-27 Credit card 3 January
## 117 2020-01-28 Credit card 7 January
## 118 2020-01-29 Credit card 4 January
## 119 2020-01-30 Credit card 4 January
## 120 2020-01-31 Credit card 3 January
## 121 2020-02-01 Credit card 3 February
## 122 2020-02-02 Credit card 3 February
## 123 2020-02-03 Credit card 4 February
## 124 2020-02-04 Credit card 2 February
## 125 2020-02-05 Credit card 3 February
## 126 2020-02-06 Credit card 5 February
## 127 2020-02-07 Credit card 4 February
## 128 2020-02-08 Credit card 4 February
## 129 2020-02-09 Credit card 2 February
## 130 2020-02-10 Credit card 2 February
## 131 2020-02-11 Credit card 3 February
## 132 2020-02-12 Credit card 1 February
## 133 2020-02-13 Credit card 4 February
## 134 2020-02-14 Credit card 3 February
## 135 2020-02-15 Credit card 10 February
## 136 2020-02-16 Credit card 2 February
## 137 2020-02-17 Credit card 5 February
## 138 2020-02-18 Credit card 0 February
## 139 2020-02-19 Credit card 3 February
## 140 2020-02-20 Credit card 5 February
## 141 2020-02-21 Credit card 0 February
## 142 2020-02-22 Credit card 3 February
## 143 2020-02-23 Credit card 3 February
## 144 2020-02-24 Credit card 2 February
## 145 2020-02-25 Credit card 5 February
## 146 2020-02-26 Credit card 2 February
## 147 2020-02-27 Credit card 6 February
## 148 2020-02-28 Credit card 1 February
## 149 2020-03-01 Credit card 3 March
## 150 2020-03-02 Credit card 7 March
## 151 2020-03-03 Credit card 6 March
## 152 2020-03-04 Credit card 1 March
## 153 2020-03-05 Credit card 5 March
## 154 2020-03-06 Credit card 5 March
## 155 2020-03-07 Credit card 1 March
## 156 2020-03-08 Credit card 3 March
## 157 2020-03-09 Credit card 5 March
## 158 2020-03-10 Credit card 8 March
## 159 2020-03-11 Credit card 5 March
## 160 2020-03-12 Credit card 6 March
## 161 2020-03-13 Credit card 2 March
## 162 2020-03-14 Credit card 5 March
## 163 2020-03-15 Credit card 5 March
## 164 2020-03-16 Credit card 0 March
## 165 2020-03-17 Credit card 2 March
## 166 2020-03-18 Credit card 3 March
## 167 2020-03-19 Credit card 5 March
## 168 2020-03-20 Credit card 3 March
## 169 2020-03-21 Credit card 2 March
## 170 2020-03-22 Credit card 5 March
## 171 2020-03-23 Credit card 2 March
## 172 2020-03-24 Credit card 2 March
## 173 2020-03-25 Credit card 1 March
## 174 2020-03-26 Credit card 6 March
## 175 2020-03-27 Credit card 4 March
## 176 2020-03-28 Credit card 4 March
## 177 2020-03-29 Credit card 0 March
## 178 2020-03-30 Credit card 2 March
## 179 2020-01-01 Ewallet 2 January
## 180 2020-01-02 Ewallet 1 January
## 181 2020-01-03 Ewallet 3 January
## 182 2020-01-04 Ewallet 1 January
## 183 2020-01-05 Ewallet 4 January
## 184 2020-01-06 Ewallet 3 January
## 185 2020-01-07 Ewallet 2 January
## 186 2020-01-08 Ewallet 5 January
## 187 2020-01-09 Ewallet 3 January
## 188 2020-01-10 Ewallet 3 January
## 189 2020-01-11 Ewallet 2 January
## 190 2020-01-12 Ewallet 6 January
## 191 2020-01-13 Ewallet 2 January
## 192 2020-01-14 Ewallet 5 January
## 193 2020-01-15 Ewallet 3 January
## 194 2020-01-16 Ewallet 1 January
## 195 2020-01-17 Ewallet 5 January
## 196 2020-01-18 Ewallet 4 January
## 197 2020-01-19 Ewallet 6 January
## 198 2020-01-20 Ewallet 6 January
## 199 2020-01-21 Ewallet 1 January
## 200 2020-01-22 Ewallet 3 January
## 201 2020-01-23 Ewallet 6 January
## 202 2020-01-24 Ewallet 6 January
## 203 2020-01-25 Ewallet 6 January
## 204 2020-01-26 Ewallet 5 January
## 205 2020-01-27 Ewallet 6 January
## 206 2020-01-28 Ewallet 3 January
## 207 2020-01-29 Ewallet 3 January
## 208 2020-01-30 Ewallet 4 January
## 209 2020-01-31 Ewallet 7 January
## 210 2020-02-01 Ewallet 1 February
## 211 2020-02-02 Ewallet 3 February
## 212 2020-02-03 Ewallet 3 February
## 213 2020-02-04 Ewallet 6 February
## 214 2020-02-05 Ewallet 5 February
## 215 2020-02-06 Ewallet 7 February
## 216 2020-02-07 Ewallet 8 February
## 217 2020-02-08 Ewallet 3 February
## 218 2020-02-09 Ewallet 4 February
## 219 2020-02-10 Ewallet 5 February
## 220 2020-02-11 Ewallet 2 February
## 221 2020-02-12 Ewallet 4 February
## 222 2020-02-13 Ewallet 2 February
## 223 2020-02-14 Ewallet 2 February
## 224 2020-02-15 Ewallet 1 February
## 225 2020-02-16 Ewallet 3 February
## 226 2020-02-17 Ewallet 4 February
## 227 2020-02-18 Ewallet 3 February
## 228 2020-02-19 Ewallet 3 February
## 229 2020-02-20 Ewallet 3 February
## 230 2020-02-21 Ewallet 1 February
## 231 2020-02-22 Ewallet 5 February
## 232 2020-02-23 Ewallet 2 February
## 233 2020-02-24 Ewallet 4 February
## 234 2020-02-25 Ewallet 6 February
## 235 2020-02-26 Ewallet 5 February
## 236 2020-02-27 Ewallet 5 February
## 237 2020-02-28 Ewallet 1 February
## 238 2020-03-01 Ewallet 6 March
## 239 2020-03-02 Ewallet 7 March
## 240 2020-03-03 Ewallet 3 March
## 241 2020-03-04 Ewallet 3 March
## 242 2020-03-05 Ewallet 10 March
## 243 2020-03-06 Ewallet 4 March
## 244 2020-03-07 Ewallet 6 March
## 245 2020-03-08 Ewallet 5 March
## 246 2020-03-09 Ewallet 3 March
## 247 2020-03-10 Ewallet 4 March
## 248 2020-03-11 Ewallet 3 March
## 249 2020-03-12 Ewallet 3 March
## 250 2020-03-13 Ewallet 5 March
## 251 2020-03-14 Ewallet 8 March
## 252 2020-03-15 Ewallet 3 March
## 253 2020-03-16 Ewallet 4 March
## 254 2020-03-17 Ewallet 2 March
## 255 2020-03-18 Ewallet 3 March
## 256 2020-03-19 Ewallet 7 March
## 257 2020-03-20 Ewallet 3 March
## 258 2020-03-21 Ewallet 4 March
## 259 2020-03-22 Ewallet 2 March
## 260 2020-03-23 Ewallet 8 March
## 261 2020-03-24 Ewallet 3 March
## 262 2020-03-25 Ewallet 3 March
## 263 2020-03-26 Ewallet 3 March
## 264 2020-03-27 Ewallet 3 March
## 265 2020-03-28 Ewallet 3 March
## 266 2020-03-29 Ewallet 4 March
## 267 2020-03-30 Ewallet 2 MarchMake plot based on data2
plot2 <- ggplot(data2,aes(x=Payment, y=Quantity)) +
geom_col(aes(fill=Month),position = "dodge") +
theme_minimal() +
labs(title = "Frequency type payment purchase on each month",
x = "Payment", y = "Quantity")
plot2
Make Interactive plot based on plot2
ggplotly(plot2)5.3 Products with the most and least sales of each gender (Produk dengan penjualan paling banyak dan paling sedikit pada setiap gender)
Make variabel “data3” to analysis visualization
data3 <- supermarket[,c("Gender","Product.line")]
data3 <- as.data.frame(table(data3$Gender,data3$Product.line))
data3 <- setNames(data3,c("Gender","Product","Quantity"))
data3## Gender Product Quantity
## 1 Female Electronic accessories 84
## 2 Male Electronic accessories 86
## 3 Female Fashion accessories 96
## 4 Male Fashion accessories 82
## 5 Female Food and beverages 90
## 6 Male Food and beverages 84
## 7 Female Health and beauty 64
## 8 Male Health and beauty 88
## 9 Female Home and lifestyle 79
## 10 Male Home and lifestyle 81
## 11 Female Sports and travel 88
## 12 Male Sports and travel 78Make plot based on data3
plot3 <- ggplot(data3,aes(x=Quantity, y=Product)) +
geom_col(aes(fill=Gender),position="dodge")
plot3
Make Interactive plot based on plot3
ggplotly(plot3)